Including Generative Mechanisms in Project scheduling using Hybrid Simulation

Scheduling is central to the practice of project management and a topic of significant interest for the operations research and management science academic communities. However, a rigour-relevance gap has developed between the research and practice of scheduling that mirrors similar concerns current in management science. Closing this gap requires a more accommodative philosophy that can integrate both hard and soft factors in the construction of project schedules. This paper outlines one interpretation of how this can be achieved through the combination of discrete event simulation for schedule construction and system dynamics for variable resource productivity. An implementation was built in a readily available modelling environment and its scheduling capabilities tested. They compare well with published results for commercial project scheduling packages. The use of system dynamics in schedule construction allows for the inclusion of generative mechanisms, models that describe the process by which some observed phenomenon is produced. They are powerful tools for answering questions about why things happen the way they do, a type of question very relevant to practic

Lessons from mixing OR methods in practice : using DES and SD to explore a radiotherapy treatment planning process

Mixing Operational Research (OR) methods is becoming more commonplace. Discrete-Event Simulation (DES) and System Dynamics (SD) are popular modelling methods previously applied to a range of situations for various purposes, which are starting to be mixed in healthcare. However, the practicalities of mixing DES and SD in practice remain unclear. Radiotherapy treatment is a complex multi-stage process where technology and best practice continue to evolve. This paper describes a project undertaken to explore the treatment planning process using mixed OR methods. It presents insights obtained through mixing OR methods within a real world project. The model development process, the role of each modelling method, and the benefits of undertaking a mixed OR methods project design are described. Lessons for mixing DES and SD, and more generally mixing OR methods, are discussed

A toolkit of designs for mixing discrete event simulation and system dynamics

In recent years there has been significant interest in multimethodology and the mixing of OR/MS methods, including Discrete Event Simulation (DES) with System Dynamics (SD). Several examples of mixing DES and SD are described in the literature but there is no overarching framework which characterises the spectrum of options available to modellers. This paper draws on a sample of published case studies, in conjunction with the theoretical literature on mixing methods, to propose a toolkit of designs for mixing DES and SD which can be implemented as a set of questions which a modeller should ask in order to guide the choice of design and inform the associated project methodology. The impetus for this work was the perceived need to transfer insight from reported practice in order to formalise how the two methods can be and have been mixed

Requirements engineering of Malaysian radiological medical emergency response simulator

The development of the Malaysian radiological medical emergency response simulator emphasized human factors according to the stakeholder's tacit and explicit knowledge. These human factors criteria were usability tested and analysed according to the socio-technical components. These analyses and interpretations were corroborated by the statistical criteria which emphasized on business process-based requirements modelling and simulation system development tools. Recent findings suggested that there were no differences of risk perceptions among these multi-agency stakeholders in the respective emergency planning framework and simulator. However, the stakeholders had differences in knowledge and experiences in the radiological and nuclear emergency planning framework (RANEPF). This paper analyses the proposed conceptual framework for further enhancement of the current RANEPF simulator. This development was in concurrence with the proposed hypothesis of the process factors and response diagram. The majority (75%) of the stakeholders and experts, who had been interviewed, witnessed and accepted that the simulator would be effective to resolve various types of disaster and resource management issues. We suggest further investigation to establish the additional functionality of the simulator as a strategist, condensed, concise, comprehensive public disaster preparedness and intervention guidelines to be a useful and efficient computer simulation

Lessons from mixing OR methods in practice: using DES and SD to explore a radiotherapy treatment planning process

Mixing Operational Research (OR) methods is becoming more commonplace. Discrete-Event Simulation (DES) and System Dynamics (SD) are popular modelling methods previously applied to a range of situations for various purposes, which are starting to be mixed in healthcare. However, the practicalities of mixing DES and SD in practice remain unclear. Radiotherapy treatment is a complex multi-stage process where technology and best practice continue to evolve. This paper describes a project undertaken to explore the treatment planning process using mixed OR methods. It presents insights obtained through mixing OR methods within a real-world project. The model development process, the role of each modelling method and the benefits of undertaking a mixed OR methods project design are described. Lessons for mixing DES and SD, and more generally mixing OR methods, are discussed

Measuring organisational performance using a mix of OR methods

Performance measurement has become an increasingly important issue in recent years. In spite of the remarkable progress that has been achieved in this area of research, many performance measurement initiatives fall short of their potential in supporting decision-making. This paper argues that adopting a multi-method approach to assessing performance has the potential to result in more comprehensive and effective performance measurement systems. To support this assertion, the paper discusses the development of a performance measurement system for a Business Tax Department, which combined the use of several operational research (OR) techniques including qualitative system dynamics, data envelopment analysis and multiple criteria decision analysis. The use of these OR techniques was influential in developing and implementing the performance measurement system and has the potential to be transferred to other contexts

Conceptualisation d'un outil d'aide à la décision pour la planification de la production de plancher de bois franc

Afin de rester compétitives dans un marché où la personnalisation de masse est omniprésente, les entreprises d’aujourd’hui n’ont d’autres choix que d’avoir une gestion efficace de leur chaîne d’approvisionnement par une vision globale des opérations de l’entreprise ainsi que des données intégrées. Dans un environnement en constante évolution comme aujourd’hui, les entreprises s’ajustent et continuent à remplir leur rôle de satisfaire la demande et de croître selon les besoins. Par contre, rares sont celles qui se permettent de revoir leur processus de prise de décision en fonction des nouveaux modèles d’affaires et nouvelles façons de faire. Parfois, il est plus simple de s’ajuster que de refaire les processus. Ce mémoire propose une solution pour le secteur manufacturier de planchers de bois franc par la conceptualisation d’un outil d’aide à la décision afin de permettre une vision globale de la production avec des données en temps réel. Ceci permettra une meilleure planification à tous les niveaux de la chaîne d’approvisionnement, en partant de l’achat des matières premières, en passant par la production, allant jusqu’au service à la clientèle. La modélisation conçue est ensuite simulée par une approche de simulation à évènements discrets afin de valider la situation actuelle, d’identifier les principaux problèmes et d’explorer des pistes de solutions futures. Les résultats démontrent le potentiel d’identification des goulots dans le processus de production et de prise de décision, et couplé à l’outil de visualisation proposé, ont le potentiel d’augmenter la performance de l’entreprise à plusieurs niveaux.In order to remain competitive in a market where mass personalization is ubiquitous, today's companies have no choice but to effectively manage their supply chain through a global vision of the operations of the company as well as integrated data. In an ever-changing environment like today, businesses are adjusting and continuing to fulfill their role of meeting demand and growing as needed. On the other hand,few are able to review their decision-making process based on new business models and new ways of doing things. Sometimes it is easier to adjust than to redo the processes. This dissertation proposes a solution for the hardwood flooring manufacturing sector by conceptualizing a decision support tool to allow global visibility of production with real-time data. This will enable better planning at all levels of the supply chain, from the purchase of raw materials, through production, to customer service. The designed modeling is then simulated by a discrete event simulation approach in order to validate the current situation, identify the main problems and explore avenues for future solutions. The results demonstrate the potential for identifying bottlenecks in the production and decision-making process, and coupled with the proposed visualization tool, have the potential to increase the company's performance at multiple levels

Methods for enhancing system dynamics modelling:state-space models, data-driven structural validation & discrete-event simulation

System dynamics (SD) simulation models are differential equation models that often contain a complex network of relationships between variables. These models are widely used, but have a number of limitations. SD models cannot represent individual entities, or model the stochastic behaviour of these individuals. In addition, model parameters are often not observable and so values of these are based on expert opinion, rather than being derived directly from historical data. This thesis aims to address these limitations and hence enhance system dynamics modelling. This research is undertaken in the context of SD models from a major telecommunications provider. In the first part of the thesis we investigate the advantages of adding a discreteevent simulation model to an existing SD model, to form a hybrid model. There are few examples of previous attempts to build models of this type and we therefore provide an account of the approach used and its potential for larger models. Results demonstrate the advantages of the hybrid’s ability to track individuals and represent stochastic variation. In the second part of the thesis we investigate data-driven methods to validate model assumptions and estimate model parameters from historical data. This commences with use of regression based methods to assess core structural assumptions of the organisation’s SD model. This is a complex, highly nonlinear model used by the organisation for service delivery. We then attempt to estimate the parameters of this model, using a modified version of an existing approach based on state-space modelling and Kalman filtering, known as FIMLOF. One such modification, is the use of the unscented Kalman filter for nonlinear systems. After successfully estimating parameters in simulation studies, we attempt to calibrate the model for 59 geographical regions. Results demonstrate the success of our estimated parameters compared to the organisation’s default parameters in replicating historical data